Abstract
Extracting biomedical triplet is one of the most important tasks in medical knowledge graph construction. Relations in complex biomedical text are overlap heavily. Although existing biomedical relation extraction methods have higher accuracy, they still have two problems. First, most of those methods hardly consider relations overlap problem. A lot of precious biomedical information is neglected. In addition, the entities in biomedical text are intensive, and the contextual information association also affects the understanding of the meaning of biomedical texts. Methods often used to encode sentence, like canonical bidirectional recurrent neural networks (BiRNN) or convolutional neural networks (CNN), are difficult to capture enough information from biomedical text. In this paper, we propose a new end-to-end triplet extraction method to address the complex triplet extraction problem in biomedical text. In our model, sentences are encoded by Recurrent Convolutional Neural Network (RCNN), which combines the advantages of BiRNN and CNN flexibly, containing more information of sentence. Experimental results on biomedical dataset and general field dataset show that our method is effective.
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Acknowledgments
This work was supported by the National key research and development program of China (No. 2017YFE0117500), National Key R&D Program of China, Grant (NO. 2016YFC0901904, 2016YFC0901604) and Science and Technology Committee of Shanghai Municipality (No. 16010500400).
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Wang, X., Li, Q., Ding, X., Zhang, G., Weng, L., Ding, M. (2019). A New Method for Complex Triplet Extraction of Biomedical Texts. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11776. Springer, Cham. https://doi.org/10.1007/978-3-030-29563-9_15
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DOI: https://doi.org/10.1007/978-3-030-29563-9_15
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